Home   >   CSC-OpenAccess Library   >    Manuscript Information
An Efficient Automatic Segmentation Method For Leukocytes
Biji G.
Pages - 83 - 89     |    Revised - 30-09-2018     |    Published - 31-10-2018
Volume - 12   Issue - 3    |    Publication Date - October 2018  Table of Contents
MORE INFORMATION
KEYWORDS
Leukocytes, Thresholding, Pixels, Peripheral Blood, Segmentation.
ABSTRACT
Blood tests are of the most important and counting of leukocytes in peripheral blood is commonly used in basic clinical diagnosis. A major requirement for this paper is an efficient method to segment cell images. This work presents an accurate segmentation method for automatic count of white blood cells. First a simple thresholding approach is applied to give initial labels to pixels in the blood cell images. The algorithm is based on information about blood smear images, and then the labels are adjusted with a shape detection method based on large regional context information to produce meaningful results. This approach makes use of knowledge of blood cell structure, the experimental result shows that this method is more powerful than traditional methods that use only local context information. It can perform accurate segmentation of white blood cells even if they have unsharp boundaries.
1 BibSonomy 
2 Doc Player 
3 Scribd 
4 SlideShare 
A. S. Samma and R. A. Salam, "Adaptation of K-Means Algorithm for Image Segmentation", International Journal of Information and Communication Engineering, 2009.
C.Di Rubeto, A.Dempster, S.Khan, B.JArra, "SEgmentation of blood images using morphological operators", Proceedings of 15th International conference on Pattern recognition, Vol.3,pp.397-400, 2000.
D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence,24:603-619, 2002.
D.Anorganingrum,"Cell segmentation with median filter and mathematical morphology operation," Proceedings on International Conference on Image Analysis and Processing, pp.1043-1046, 1999.
F. H . A. Jabar, W. Ismail, K. A Rahim, and R. Hassan, "K-Means Clustering For Acute Leukemia Blood Cells Image", Proceedings of the International Conference on Soft Computing and Computational Mathematics (ICSCCM), 2013.
G.Cao, C.Zhong, L.Li and J.Dong, "Detection of Red blood cell in Urine micrograph," in ICBBE, 2009.
H. Zhang, J. E. Fritts, S.A. Goldman, "Image Segmentation Evaluation: A survey of unsupervised methods", IEEE proceedings on Computer Vision and Image Understanding, Volume 110, Issue 2, Pages 260- 280, May 2008.
H.Ramoser, V.Laurian, H.Bischort and R.Ecker, "Leukocyte segmentation and classification in blood smear images", Proceedings in IEEE Engineering in Medicine and Biology, Shanghai 2005.
H.T.Madhloom, S.A.Kareem, H.Ariffin, A.A.Zaidan, H.O Alanazi and B.B.Zaidan, "An automated white Blood cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic threshold", Journal of Applied Sciences, Vol.10, No.11, pp.959-966, 2010.
J. Freixenet, X. Munoz, D. Raba, J. Marti, X. Cufi, "Yet another survey on image segmentation: Region and boundary information integration." ,Notes in Computer Science Volume 2352, pp 408-422, 2002.
J.Theerapattanakul, J.Plodpai and C.Pintavirooj, "An efficient method for segmentation step of automated white blood cell classifications," in TENCON, Bangkok 2004
M. Kunt, M. Benard, and R. Leonardi, " Recent results in high compression image coding" IEEE Transactions Circuits System, vol.34, 1987, pp.1306-1336.
M.B.Jeacocke, B.C.Lovell, "A multi-resolution algorithm for cytological image segmentation," Proceedings of International conference on second Australian and New Zealand Intelligent Information Systems, pp.322-326, 1994.
N. H. Harun, M. Y. Mashor, and R. Hassan, "Automated Blasts Segmentation Techniques Based on Clustering Algorithm for Acute Leukaemia Blood Samples", Journal of Advanced Computer Science and Technology Research 1. 96-109, 2011.
P. Filipczuk, M. Kowal, A. Obuchowicz, "Automatic Breast Cancer Diagnosis Based on K-Means Clustering and Adaptive Thresholding Hybrid Segmentation", Image Processing and Communications Challenges. 3, 295-302, 2011.
T. Z. Muda, R. A. Salam, "Blood cell image segmentation using hybrid K-Means and median-cut algorithms" IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2011.
Mrs. Biji G.
Dept of Electrical Engineering Govt Engineering College Bartonhill Thiruvananthapuram - India
biji2engg@gmail.com


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS